Claude Sonnet 5

Post by wiker » Thu, Jul 02, 2026, 11:17 AM UTC

keeda wrote: post #519

hedora wrote: post #347

Earlier quoted context omitted.

You’re trying to apply value based pricing (infinite margin upside) to a commodity. Pre-bubble pricing: $1400 gets a 128GiB iGPU optimized for inference. Glm and kimi need 800-1000GiB. Call it 1TiB. The $1400 boxes could be ganged into sets of 4-8, with a switch. Call the switch $1000. Each box has a TDP of 250W. 8 x 250/120V = 16.666A, or one household circuit in the US, so no new power infrastructure is needed. $14…

Right, that number is more of an estimate of the value proposition of the entire AI industry rather than projections of revenue or valuations... it's essentially an estimate on how much the market could theoretically bear. Whether the companies can capture that value is, to your point, rightly a different question. I do think open weight and other competitor models, especially with better harnesses, will play a signi…

Post by troglodytetrain » Thu, Jul 02, 2026, 2:18 PM UTC

keeda wrote: post #253

everforward wrote: post #159

Earlier quoted context omitted.

There’s no way to justify their valuations if they get downgraded to a pair programming tool. They need fully agentic stuff to work and replace human engineers to even come close. Offhand, I’m not even certain whether a model like that could justify the constant retraining we’re doing on the agentic models. It doesn’t make a lot of sense to spend millions or billions on training to reduce hallucinations by 0.3% if yo…

Some napkin math -- total global labor compensation is about 50% of the GDP, which puts it in the USD 50 - 60 Trillion range: https://ourworldindata.org/grapher/labor-share-of-gdp This source claims that knowledge workers alone (probably because they are paid much more) account for 35 - 50 Trillion of that: https://github.com/danielmiessler/Substrate/blob/main/Data/K... If LLMs can boost their productivity even by an…

Post by flumes_whims_ » Thu, Jul 02, 2026, 4:50 PM UTC

lawrjone wrote: post #739

sixtyj wrote: post #132

Earlier quoted context omitted.

I have tried to rewrite an article with GLM-5.2 and with Sonnet 4.6. Completely different results as LLM is non-deterministic. But GLM-5.2 made a lot of subtle mistakes that needed to be corrected by hand. On the opposite, Sonnet found and corrected all mistakes in the second round. Similar situation was with planning and coding. GLM-5.2 seems to be good “on paper” but the real usage results was different. And I am n…

We have found similar when plugging GLM 5.2 into actual benchmarks in our product. The open-source models are really dialled into the public benchmarks, until you try them in context you won't have a solid idea of how they perform (Sonnet is a higher quality model than 5.2, both in prose, reasoning, and alignment).

Post by icepush » Fri, Jul 03, 2026, 9:57 AM UTC

scarmig wrote: post #355

bloppe wrote: post #313

Earlier quoted context omitted.

Idk why you're perceiving silence. Feels to me like this is the main thing people talk about nowadays.

It has to do with the scope of what they're discussing. It seems extraordinarily small: e.g. what if AI increases productivity growth by 0.4%? Do data centers use too much water? Are AIs racist when reviewing resumes? The frontier labs, on the other hand, are thinking about replacing all human labor, ending death, and the risk of it causing human extinction. Most of the apparatus we're talking about approach it very…

Post by breezybottom » Fri, Jul 03, 2026, 8:42 PM UTC

pigpop wrote: post #816

breezybottom wrote: post #473

Earlier quoted context omitted.

You think AI tech doesn't use rare earth metals?

Rare earth metals aren't rare, it's a misnomer.